Mastering LLM Limitations: Context Windows, Inference Engines, and Beyond

Large Language Models (LLMs) have transformed how we process and analyze vast amounts of text, but their capabilities come with critical limitations, particularly when handling long documents or sets of documents. These limits can lead to errors in legal reviews, research summaries, or code analysis—issues I've encountered firsthand. One of the primary constraints is the context window, which defines the amount of text an LLM can process at once. Understanding these limits is essential for effectively using LLMs in tasks like summarizing or querying extensive documents.

What is Context Size?

The context size of an LLM refers to the maximum number of tokens it can process in a single input. Tokens are the units LLMs use to break down text—typically, an average English word equates to about 1.3-1.5 tokens, depending on its length and complexity. Token counts can vary by model; use tools like Hugging Face's tokenizer playground to check specifics. For example:

These estimates highlight how quickly document sizes can exceed the context window of many LLMs, especially for lengthy texts like novels or complex legal agreements.

Evolution of Context Windows

Context window sizes have grown significantly in recent years. For instance, Llama 2 was limited to a 4,000-token context window, which could handle a short letter but struggle with longer texts like a 20-page story. Llama 3.1 expanded to 128,000 tokens, enabling the processing of much larger documents, such as a short story or a small novel. The Llama 4 models, released in April 2025, push this further, offering context windows up to 10 million tokens in variants like Llama 4 Scout, theoretically capable of handling entire novels or extensive document sets. For comparison, models like Gemini 2.0 or Claude 3.5 also reach 1M+ tokens, but Llama 4's 10M sets a new benchmark.

However, a large context window alone doesn’t guarantee success—it's theoretically capable, but real-world issues like increased latency or memory demands can arise. Other limitations, particularly those tied to the inference engine, can significantly restrict performance.

Inference Engine Limitations

The inference engine — the software that runs the LLM—often imposes its own token limits. For example, Ollama, a popular inference engine, defaults to a 2,048-token limit, but this can be customized via parameters like num_ctx in the API (e.g., to 128K for Llama 3.1), though hardware constraints may apply. Even if you use a model like Llama 4 with a 10-million-token context window, the inference engine may truncate the input to its own limit, discarding earlier parts of the document. This can lead to critical information loss.

Example: Analyzing Loan Documents

Consider analyzing a lengthy loan agreement to answer a simple question: "Who is the Borrower?" Suppose the agreement defines "Borrower" (a legal term) in its very first sentence, but the document spans 50,000 tokens. If the inference engine truncates everything except the last 2,048 tokens, the definition of "Borrower" may be lost. As a result, the LLM might incorrectly respond that the Borrower is not defined in the document, even though the term appears repeatedly in later sections. If the engine supported the full context, however, the LLM could cross-reference the definition accurately. This truncation issue underscores the importance of aligning the model’s context window with the inference engine’s capabilities and the task’s requirements. This isn't just for loans—think medical records, patents, or codebases where early definitions are crucial.

Optimizing for Larger Contexts

To mitigate these limitations, careful optimization of the LLM and inference setup is necessary. Several strategies can help maximize the effective context size:

• Choosing a smaller model: Models with fewer parameters (e.g., smaller Llama variants) use less GPU memory, leaving more room for the KV cache (key-value cache used in attention mechanisms). Pros: Less memory use; Cons: Potentially lower accuracy on complex tasks.

• Reducing quantization for KV cache: Lowering the precision of the KV cache (e.g., from 16-bit to 8-bit) reduces memory usage per token. Pros: Saves VRAM; Cons: Slight performance drop. Tools like Ollama allow easy quantization changes via flags (e.g., --quantize q8_0).

• Using models with fewer attention heads: Attention heads contribute to memory demands, so models with fewer heads can process more tokens within the same memory constraints. Pros: More efficient for long contexts; Cons: May reduce model expressiveness.

• Leveraging GPUs with more VRAM: Higher video RAM (VRAM) allows the inference engine to handle larger token limits without truncation. Pros: Direct scalability; Cons: Requires more expensive hardware.

Use monitoring tools like nvidia-smi to track VRAM usage during inference. These optimizations, while effective, are often esoteric and require technical expertise beyond the average user’s knowledge.

Data Search Strategies: RAG and Its Limits

Another approach to handling large documents is Retrieval-Augmented Generation (RAG). RAG involves breaking a document into smaller chunks, indexing them, and retrieving only the most relevant chunks to answer a query. For example, a loan agreement might be split into a dozen chunks, and RAG would select the most relevant ones based on the question.

However, RAG has its own limitations. In the loan document example, if the question "Who is the Borrower?" relies on information from the first sentence, but the retrieved chunks only cover later sections where "Borrower" is referenced without definition, the LLM may still fail to provide the correct answer. RAG is most effective when the relevant information is contained within the retrieved chunks, but it struggles when critical context is scattered or located outside the selected portions. Also, for large documents, the RAG context size (number of chunks selected by the RAG system multiplied by the size of the chunks) can still exceed the model's context window size, resulting in some of the chunks being ignored in the response to a query. Again, if the “Borrower” is defined in chunk 1 of 40 chunks but only 32 chunks fit in the system, the first 8 chunks can be dropped from the analysis resulting in the system not knowing the definition of the “Borrower”.

For scattered info, advanced techniques like hierarchical RAG (summarizing chunks first) or re-ranking can help ensure key info isn't missed. Effective chunking and embedding strategies (e.g., semantic splitting) can also reduce this risk.

Context Limits Within the LLM Framework

Beyond model and inference engine constraints, the LLM framework itself can introduce additional limitations. When troubleshooting the "Who is the Borrower?" issue, I encountered unexpected behavior in AnythingLLM, a common framework for managing LLM workflows. To diagnose the problem, I plotted GPU VRAM usage against the number of RAG chunks processed for a document set. Surprisingly, VRAM utilization plateaued at around 50 chunks, despite the model’s 128,000-token context window and my optimized Ollama instance.

In my setup, analysis of the open-source code of AnythingLLM on GitHub revealed a potential limit in the framework: it appeared to restrict RAG to the lesser of 50 chunks or one-tenth of the total chunks in the RAG database. This cap prevented the system from leveraging the full context window, effectively limiting the amount of document data available for processing. In the loan document example, this meant that even if the Borrower’s definition was in the first chunk, it might not be included if it fell outside the 50-chunk limit. (For the latest details, check the repository: Anything LLM, as limits may be configurable or version specific.)

To resolve this and finally identify the Borrower, I’ll need to invoke assistance to modify the AnythingLLM code, remove the chunk limit, and rebuild the Docker container. However, this experience highlights that even with an optimized model and inference engine, framework-specific constraints can still impede performance. There may be additional limits to uncover, as this exploration is far from complete. Other frameworks like LangChain or Haystack have configurable top-k retrieval limits, so always review documentation

Conclusion

Navigating the constraints of LLMs requires a deep understanding of multiple layers: the model’s context window, the inference engine’s token limits, and the framework’s internal restrictions. While advancements like Llama 4’s million-token context windows, inference engine optimizations, and RAG strategies push the boundaries of what’s possible, hidden limits within frameworks like AnythingLLM can still derail efforts.

Start by benchmarking your setup with sample documents—tools like Ollama's API or AnythingLLM's logs can reveal hidden limits. As models like Llama 4 evolve, these limits will shrink, but proactive optimization remains key.

The lesson is clear: Know your limits—or at least the limits of your LLM model, inference engine, and framework. By systematically identifying and addressing these constraints, you can design more effective workflows and unlock the full potential of LLMs for complex document analysis.